Fig. REF  shows the result of DeepJSCC-Q trained for different \(\text{SNR}_{\text{Train}}\)  and modulation order \(M\)  and tested over a range of channel SNR values for the AWGN channel case.
For all M-QAM constellations shown here, DeepJSCC-Q exhibited graceful degradation of image quality with decreasing channel quality.
This is similar to the DeepJSCC results in [1]}, but we are able to obtain the same behavior despite being constrained to a finite constellation.
Moreover, when compared with the separation-based results, the DeepJSCC-Q 4096-QAM model performed almost exactly on the envelope of all the separation-based schemes.
Although the DeepJSCC-Q models trained with modulation orders \(M=16,64\)  did not perform as well as the separation-based schemes, increasing the modulation order at those SNRs can improve the performance of DeepJSCC-Q, as shown in Fig. REF .
We see that when we employ a modulation order of \(M=4096\)  for \(\text{SNR}_{\text{Train}} = 7, 10\) dB, DeepJSCC-Q beats the separation-based schemes convincingly.
This shows that, the end-to-end optimization of DeepJSCC-Q is fundamentally different from separation-based schemes, as the end-to-end distortion is directly affected by the channel distortion rather than the successful decoding of the channel code for a given source code rate.
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